Genetic evolution of radial basis function centers for pattern classification

Man Wai Mak, Kin Wai Cho

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)


This paper proposes to embed a genetic algorithm (GA) in the traditional learning algorithm of radial basis function (RBF) networks. Each function center of an RBF network is encoded as a binary string, and the concatenation of the strings forms a chromosome. In each generation cycle, the GA determines the center locations. Then the K-nearest neighbor heuristic and singular value decomposition are applied to find the function widths and output weights of each network, respectively. The performance of the proposed algorithm is evaluated on three problem sets. The results show that networks with centers found by the proposed algorithm achieve a lower mean squared error and a higher classification accuracy than networks with centers found by the K-means algorithm. The paper explains the findings by demonstrating that the best center locations may not necessary be located inside the input clusters.
Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Number of pages5
Publication statusPublished - 1 Jan 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, United States
Duration: 4 May 19989 May 1998


ConferenceProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
Country/TerritoryUnited States
CityAnchorage, AK

ASJC Scopus subject areas

  • Software

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